Projector imaging system with auto-homing tunable filter

ABSTRACT

Some embodiments are directed to an imaging that includes an image sensor; a tunable filter; and a controller operatively connected to the tunable filter and to the image sensor. The imaging system is configured to: tune the tunable filter to a plurality of filter states. The image sensor acquires, at each state of the plurality of states, an image of an object, to provide different images of the object. The controller calculates a state related score, for each state that is indicative of one or more properties of at least one subset of pixels of the at least one image acquired at the state, to provide a plurality of state related scores; and determines, based on at least one of the plurality of state related scores, a desired state of the tunable filter that satisfies a desired state related score criterion; and sets the tunable filter.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a national phase filing under 35 C.F.R. § 371 of andclaims priority to International Application No. PCT/IB2018/057250,filed on Sep. 20, 2018, which claims the priority benefit under 35U.S.C. § 119 of U.S. Provisional Patent Application Nos. 62/623,846 and62/560,690 filed on Jan. 30, 2018 and Sep. 20, 2017 respectively, thecontents of each of which are hereby incorporated in their entireties byreference.

BACKGROUND

Some embodiments of the presently disclosed subject matter relate ingeneral to imaging systems or devices and in particular to infrared (IR)projector imaging systems or devices.

A standard IR projector imaging system or device normally includes atleast one IR light source (e.g. LED or VCSEL) and an IR imager thatincludes an image sensor (e.g. CMOS) in series with a fixed IR band pass(BP) filter which is compatible with a specific wavelength of the IRlight source. The IR images may be designed for example to work in thenear IR range, where the term “near IR” refers to a wavelength range ascommonly understood in the art, for example the range 750-1400 nm. Knownfixed IR BP filters are optimized such that they transmit most of thelight emitted from the light source while minimizing the leakage ofambient light into the sensor. While minimized ambient light leakage,such fixed BP filters still allow significant ambient light to reach theimage sensor.

The emission spectra of any light source vary due to factors such asmanufacturing tolerances and thermal drift. For example, the wavelengthassociated with the peak in the spectra of a Princeton Optronics LargeDivergence 945 nm VCSEL Array Module (PCW-SMV-2-W0945-1-D60-45) couldvary by ±10 nm due to manufacturing tolerances and by an additional ±3nmdue to thermal drift over a ±40° C. change in ambient temperature. Thecorresponding manufacturing tolerances and thermal drift values for anOsram 810nm LED SFH 4780S are ±13 nm and ±12 nm, accordingly.

In order to assure that a sufficient amount of the light source energyreaches the image sensor of the system, the light source's possiblespectral shift should be accounted for, and thus band-pass filters insuch applications are generally designed with a full width at halfmaximum (FWHM) equal to at least the sum of the FWHM of the light sourcespectra and the amount of possible spectra shift. However, using aband-pass filter with a FWHM greater than that of the light sourceincreases the ambient light leakage into the sensor and may results in alow system performance.

As an example, the nominal spectra of Osram's 810 nm LED SFH 4780S isdepicted in FIG. 1A. Its FWHM is approximately 30 nm and the total driftis ±25 nm. Combined with a typical sun ambient lighting at sea level,given in FIG. 1B, its resulting spectra is shown in FIG. 1C. That samefigure also shows such a combination for the case in which the LEDspectrum has a drift of +25 nm. In view of the problem explained aboveand in order to accommodate for any possible drift in the LED spectrum,an IR band-pass filter needs then to be designed with a FWHM value of atleast 80 nm—which is more than double the nominal LED FWHM value.

A common figure of merit for ambient light leakage is the Energy Ratio(ER), defined as the ratio between the energy in the wavelength rangecorresponding to the FWHM of the light source and the total energyreaching the image sensor. However, measuring ER directly in such systemis not feasible without apriori knowledge of the exact ambient lightcharacteristics. Thus, this measure is usually only used to assess theperformance of such systems under laboratory conditions.

FIG. 1D depicts the spectra resulting from the combination of thenominal lighting spectrum of the LED of FIG. 1A with the ambientlighting spectrum of FIG. 1B. Black filled areas represent the lightleakage through a band pass filter with a FWHM of ca. 80 nm required toaccommodate a possible drift in the LED spectra. The ER value calculatedbased on the total amount of light passing through such a filter and theamount of light leaking through it is expected to be approximately 50%,which is considered to be low, causing poor system performance.

Therefore there is a need for, and it would be advantageous to have aprojector imaging system including a tunable IR band pass filter (alsoreferred to simply as “tunable filter”) that has a similar FWHM to thatof a respective light source and which can be adjusted according to adrift in the spectra of the light source, thus enabling to transmit thesame amount of light source energy while reducing the leakage of ambientlight in comparison with the performance of an equivalent fixed filterwith optimal design.

Furthermore, computer vision cameras can be classified as cameras thatoutput images later processed by a processor. These cameras includeautomotive cameras, security cameras etc, and in some cases the goal isnot to capture a color image (including all or most RGB planes). As mostcameras, the image sensors have a pre-defined color filter array whichmay be included of two or more colors. These cameras, inherently sufferfrom this constraint of capturing only pre-defined colors, trade offresolution with color information (e.g., 25% pixels sample red and 75%of pixels sample white/clear). Also, special algorithms of de-mosaickingare needed to interpolate the different color planes intofull-resolution images. This process usually requires hardwareacceleration and induces latency which undesirable for moving systems.

Moreover, standard computer vision and image processing algorithmsusually rely on the color channels supported by the color filter arrayon the image sensor. Common examples include RGB cameras (common inmobile handsets, in which 3 color planes are captured) and red-clearcamera (common in automotive cameras, 2 color planes). It is well knownthat these algorithms could improve their performance if additionalinformation will be available, such as additional color planes.

SUMMARY

Some embodiments of the presently disclosed subject matter thereforerelate to imaging systems, including projector imaging systems, whichinclude auto-homing tunable filters, and to methods of use of suchsystems and tunable filters.

As used herein, “auto-homing” refers to automatic tuning of the filterto a filter state. As disclosed herein below auto-homing is implementedin a manner that helps to improve performance of the imagining system.For example, by increasing (e.g. maximizing) the amount of lightoriginating from the projector that reaches the image sensor of theimaging system and reducing (e.g. minimizing) leakage.

According to the presently disclosed subject matter, a tunable filtersuch as a tunable IR band pass filter is provided, having a similar FWHMto that of the light source, which can be adjusted according to a driftin the spectra of the light source, thus enabling to transmit the sameamount of light source energy while reducing the leakage of ambientlight in comparison with the performance of an equivalent fixed filterwith optimal design. That is, the tunable IR band pass filter canauto-home into a filter state that allows it to increase transmission ofthe light emitted from a light source while reducing leakage of ambientlight into an image sensor. Moreover, projector imaging systems workingin wavebands other that IR (for example visible (VIS)) may similarlybenefit from band pass filters in the respective wavebands that areoptimized to increase transmission of light emitted from a light sourcewhile minimizing leakage of ambient light into an image sensor.

In some exemplary embodiments, there are provided projector imagingsystems including a light source for emitting light in a requiredwavelength band, an image sensor, a tunable filter configured toauto-home into a particular filter state that reduces (e g minimizes)ambient light leakage into the image sensor, and a controlleroperatively connected to the tunable filter and to the image sensor andconfigured to determine the particular filter state, to control thetunable filter and to operate the imaging system for capturing an image.

In some embodiments, the required wavelength band includes an IRwavelength band.

In some embodiments, the IR wavelength band includes a near-IRwavelength band.

In some embodiments, the required wavelength band includes a visible(VIS) wavelength band.

In some embodiments, the controller may be configured to determine theparticular filter state includes a configuration to sweep the tunablefilter through a plurality of tunable states in the vicinity of anominally predetermined state x₀ and to find a local maximum of a costfunction I_(T)(x) corresponding with the particular filter state.

In some embodiments, the controller is further configured to set thetunable filter to the particular filter state prior to operating theimaging system for capturing an image.

In some embodiments, the tunable filter is a MEMS tunable filter.

In some embodiments, the controller may be configured to sweep the MEMStunable filter through a plurality of tunable states in vicinity of anominal predetermined state x_(o) and to find a local maximum of a costfunction I_(T)(x) corresponding with the particular filter state.

In some embodiments, the controller is further configured to set theMEMS tunable filter to the particular filter state prior to operatingthe imaging system for capturing an image.

According to another example there may be provided an imaging system mayinclude an image sensor; a tunable filter; and a controller operativelyconnected to the tunable filter and to the image sensor and configuredto tune the tunable filter through a plurality of filter states within acertain wavelength band; calculate for an image output of each state ofthe plurality of states a respective score; select based on thecalculated score a particular filter state out of the plurality offilter states that satisfies a desired state related score criterion;and set the tunable filter to the desired state that minimizes ambientlight leakage into the image sensor; and operate the imaging system forcapturing an image while the tunable filter may be tuned in theparticular state.

In some examples the imagining system may include a light sourceconfigured for transmitting light in a desired wavelength band, smallerthan the certain wavelength band, wherein the certain wavelength bandmay be determined according to the desired wavelength band of the lightsource and an expected drift around the desired wavelength band.

In some examples the score may be indicative of ambient light absorbedby the image sensor relative to light in the desired wavelength bandabsorbed by the image sensor.

The score may be an output value of a cost function and the controllermay be configured to calculate a cost function output value for each ofthe plurality of states.

There may be provided an imaging system that may include an imagesensor, a tunable filter, and a controller operatively connected to thetunable filter and to the image sensor and configured to determine aparticular filter state that minimizes ambient light leakage into theimage sensor, and operate the imaging system for capturing an image.

The imaging device may include a light source for emitting light in arequired wavelength band.

The required wavelength band includes an infrared (IR) wavelength band.

The IR wavelength band includes a near-IR wavelength band.

The required wavelength band includes a visible (VIS) wavelength band.

The controller may be configured to sweep the tunable filter through aplurality of tunable states in vicinity of a nominal predetermined statex and to determine a local maximum of a cost function I_(T)(x)corresponding with the particular filter state.

The controller may be configured to set the tunable filter to theparticular filter state prior to operating the imaging system forcapturing an image.

The tunable filter may be configured as a MEMS tunable filter.

There may be provided a method for acquiring images by an imaging systemthat may include an image sensor and a tunable filter, the method, mayinclude determining a particular filter state that minimizes ambientlight leakage into the image sensor; setting the tunable filter to theparticular filter state; and obtaining image data with the filter set tothe particular filter state.

The determining of a particular filter state includes configuring acontroller of the projector imaging system to determine the particularfilter state.

There may be provided a method for setting a tunable filter, the methodmay include tuning, at different points in time, a tunable filter todifferent states for passing different frequency ranges; at each stateacquiring at least one images of an object, by an image sensor, toprovide different images of the object, wherein the acquiring occurswhile a radiation source illuminates the object; and selecting aselected setting of the tunable filter based on one or more propertiesof one or more images of the different images.

The method may include selecting the selected image to fulfill a desiredrelationship between (a) radiation from the object that originated fromthe radiation source and may be sensed by the image sensor, and (b)ambient radiation sensed by the image sensor.

The desired relationship may include having a maximal amount ofradiation from the object that originated from the radiation source anda minimal amount of the ambient radiation

The desired relationship may include a maximal ratio between (a) anintensity of the radiation from the object that originated from theradiation source and (b) an intensity of the ambient radiation.

The tunable filter may be a narrowband filter having a bandwidth thatmay be smaller than nanometer.

The different states consist of a first state and a second state.

The different states may include at least three states.

The one or more properties may be a signal to noise ratio.

The one or more properties may be a contrast.

The selecting of the setting may include applying a cost function on theone or more properties of a subset of pixels of the one or more images.

The cost function may be an average intensity of pixels of the subset.

The method may include selecting pixels that belong to the subset.

The tunable filter may be a narrowband filter having a bandwidth thatequals a bandwidth of the radiation.

The tunable filter may be a narrowband filter having a bandwidth thatdiffers from a bandwidth of the radiation.

The method may include acquiring images while the tunable filter may beset to the selected setting.

The radiation source may exhibits a frequency drift.

There may be provided an imaging system that may include an imagesensor, a tunable filter, and a controller operatively connected to thetunable filter and to the image sensor and configured to tune, atdifferent points in time, a tunable filter to different states forpassing different frequency ranges; wherein the image sensor may beconfigured to acquire, at each state, at least one image of an object,to provide different images of the object; wherein the acquiring occurswhile a radiation source illuminates the at least part of the object;and wherein the controller may be further configured to select aselected setting of the tunable filter based on one or more propertiesof one or more images of the different images.

Imaging systems may include a tunable spectral filter placed between thedetector (either a single pixel or a pixel matrix) and the scene. Theseimaging systems may include a tunable spectral filter for eitherimproving its performance or enhancing its functionality.

Examples an imaging system may include a detector in which includes atunable spectral filter whereas the filter maximizes the ratio betweenlight that is projected by an illuminator on the scene and ambientlight. This class of imaging systems may include an IR camera thatshould collect the light emitted by a VCSEL/LED illuminator where thedetector is a CMOS image sensor.

The imaging system may be included in a LIDAR system in which thedetector (e.g., SiPM, SPAD etc.) collects a light beam (that may bestirred), emitted by an illuminator such as a Laser.

The imaging systems may have an integrated tunable spectral filter.

The imaging system may often be required to operate in uncertaintyconditions. Such uncertainty may include for example CW variability ofthe illuminator, different ambient lightings (intensity and/or spectrum)and others. Thus, having a tunable spectral filter which is adaptive andcan accommodate for uncertainty can improve overall system performance.

The adaptiveness of such an imaging system may require the detector tocollect light and a processor to perform some statistical analysis ofthe measurement. The nature of this analysis can vary across differentsystems that need to perform different tasks. Then, the spectral filtercan be tuned once or more until the statistical analysis eithermaximizes a figure of merit or have reached a pre-determined acceptablelevel.

For example, for an IR camera that should capture a scene illuminated byan LED, the statistical analysis will measure the intensity.

An imaging system may be included in a system which navigates based onan input from a camera and for that purpose requires to find edges ofobjects. This system would benefit from tuning the spectral filter toyield the spectral profile in which the objects' edges are sharpest(highest image contrast).

The imaging system may be used in an environment which undergoes fastchanges ambient light intensity (e.g., when a car enters and exits atunnel). In such case, the spectral filter will be tuned to thewavelength which minimizes the ambient light intensity variation.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting examples of some embodiments of the presently disclosedsubject matter are described below with reference to figures attachedhereto that are listed following this paragraph. The drawings anddescriptions are meant to illuminate and clarify some embodiments of thepresently disclosed subject matter, and should not be consideredlimiting in any way. In the drawings:

FIG. 1A depicts an approximate curve of the nominal spectra of an Osram810 nm LED SFH 4780S, together with the desirable FWHM of anaccompanying band pass filter;

FIG. 1B depicts the typical spectrum of the sun ambient lighting at sealevel;

FIG. 1C depicts the spectra resulting from the combination of thenominal and drifted lighting of the LED of FIG. 1A with the ambientlighting of FIG. 1B;

FIG. 1D depicts the spectra resulting from the combination of thenominal lighting of the LED of FIG. 1A with the ambient lighting of FIG.1B;

FIG. 2A illustrates graphically some embodiments of a projector imagingsystem disclosed herein, according to some examples;

FIG. 2B shows schematically the structure of a controller in theprojector imaging system of FIG. 2A, according to some examples;

FIG. 2C shows exemplary transmission curves obtained with the system ofFIG. 2A for 3 different tunable filter states with center wavelengths of790, 810, and 830 nm;

FIG. 3 presents a flow chart illustration of operations carried outaccording to some examples of the presently disclosed subject matter;

FIG. 4 depicts an example of a cost function I_(T) as a function of thetunable filter's state;

FIG. 5 illustrates an example of a sequence of images;

FIG. 6 illustrates an example of a system;

FIG. 7 illustrates an example of a method;

FIG. 8 illustrates an example of a method;

FIG. 9 illustrates an example of a method;

FIG. 10 illustrates an example of a method;

FIG. 11 illustrates an example of a method;

FIG. 12 illustrates an example of a method;

FIG. 13 illustrates an example of a method;

FIG. 14 illustrates an example of an imaging system; and

FIG. 15 illustrates an example of an imaging system.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Any reference to an image of an object may be applied mutatis mutandisto an image of a part of the object.

Any reference to an image may be applied mutatis mutandis to any numberof pixels of the image.

FIG. 2A illustrates graphically some embodiments of a projector imagingsystem or device 200 disclosed herein. System 200 includes a lightsource (e.g. LED, VCSEL) 202, a tunable band pass filter (e.g. aMEMS-based tunable etalon) 204, an image sensor (e.g. CMOS) 206, and acontroller 208. While applicable for imaging in various wavelengthbands, the following description of the system and method of use arefocused for simplicity on the IR band. The system according to thepresently disclosed subject matter, enables to augment (and possiblyoptimize) its image output (specifically IR image output) by decreasingthe ambient lighting leakage into the image sensor and by increasing IRabsorption at the image sensor. Controller 208 is operatively connectedto tunable filter 204 and to image sensor 206, and configured to controlthe tunable filter, operate the imaging device for capturing an image,and determine a selected filter state which provides reduced (e gminimized) ambient light leakage into the image sensor, as described inmore detail below.

An example of a tunable filter that can be used for example forsequential imaging is an etalon. An etalon includes two parallelmirrors. The spectral transmission profile is determined by the gapbetween the mirrors. The tuning of a voltage applied to etalon tunes thegap between the mirrors (which provides a so called “optical cavity”)and, in turn, tunes the spectral transmission profile. The two mirrorsmay be for example a semi-transparent front mirror and asemi-transparent back mirror. In some examples, the back mirror may be,for example, stationary while the front mirror may be movabletoward/away from the back mirror in order to change the distance(optical cavity) between them, and thereby tune the spectraltransmission profile. Tunable filters can includes for example,microelectromechanical system (MEMS) tunable filters such as the MEMSFabri-Perot filter. An example of such a tunable filter is disclosed inpatent applications published as WO 2018/092104 and WO 2017/009850 ofthe Applicant, which is incorporated herein by reference in itsentirety.

FIG. 2B illustrates schematically in a block diagram the structure of acontroller 208. The controller may include a filter driver 210responsible for tuning the filter to a filter state, an imageacquisition module 212 responsible for operating and obtaining imagesfrom the image sensor, a storage memory module 214 that may serve totemporarily store the acquired images, and an optimizer 216 responsiblefor calculating the cost function for each of the stored images and toaccordingly determine and select an optimized filter state. Memorymodule 214 can also be used for storing the wavelength range of thelight source and an expected drift range, which can be updated using anoptional application input interface (API) module 218, based on thespecific type of light source.

The optimization or augmentation of performance in imaging system 200can be achieved for example, by defining a measurable figure of merit(“cost function”) I_(T)(x), which is optimized by sweeping the tunablefilter through several of its tunable states in vicinity of a nominalpredetermined state x₀ (assuming no drift in the light source) and byfinding the local maximum of I_(T)(x) corresponding with a state x_(m)of the filter. Vicinity may be regarded based on an actual or expectedfrequency drift of the radiation source, may be regarded as within 10%,20%, 30%, 40% from the nominal state, and the like.

The several tunable states may be two states, three states, four statesor more than two states. For example—the tunable filter may operate in abinary manner—and the sweeping may include or can consist of operatingin a first state and then operating in another state. The first statemay provide a band pass filter at the range of 600-800 nm and the secondstate may provide a band pass filter at the range of 300-500 nanometers.Other bandpass ranges may be provided.

The number of the several tunable states may depend on the type oftunable filter. For example, a tunable filter may exhibit a tradeoffbetween cost of the tunable filter (or size of the tunable filter) andfunctionality—as reasonable cost tunable filters may be limited in thenumber of different states.

In some examples, the relevant states range (wavelength band or range)for sweeping the filter is calculated in advance according to thewavelength of the IR light source and the expected drift of the lightsource. For example, for a 810 nm light source with an expected spectradrift of up to ±25 nm, the relevant filter states range would correspondto a transmission peak range in the range of 785-835 nm. This range canthen be divided into N−1 equal intervals, and the filter would then beswept through each of the N corresponding states, where for each statethe cost function I_(T)(x) is calculated and registered.

In one example, the states range can be provided to controller 208 asinput from an external resource. In another example, controller 208 canbe configured to calculate the states range based on the wavelengthrange of the light source and an expected drift range. Data indicativeof the state range can be stored for example, in memory module 214.

According to one example, I_(T)(x) is defined as the system totalintensity signal which is a function of all or most intensity readingsin a subset of the sensor's pixels.

The subset of pixels may be selected in any manner, may be selected inadvance, may be selected after image processing, and the like. Theselection of the subset may change over time, and the like.

The subset of pixels may be of any number of pixels, of any shape and/orof any size. The subset may be selected based on an image processing ofone or more images, may be based on an intensity histogram, may be basedon previous selections of subsets of pixels, may be based on informationnot included in the one or more images (such as a time of day and/orlocation—as ambient light may change over the day, may change betweenone country to another), and the like.

The subset of pixels may include a predefined number of pixels locatedat the center of the image—or in any other location. The subset ofpixels may include pixels that are adjacent to each other, may includepixels that are spaced apart from each other, may include differentspaces apart groups of pixels wherein each group of pixels includespixels that are adjacent to each other.

The subset may include pixels that exhibit a certain property—such aspixels that belong to a region of the image that exhibits a certainproperty—such as being of a highest intensity region. The highestintensity region may include the highest intensity pixels, may be of ahighest average intensity out of all or most regions of the image, andthe like.

In some examples an image may be acquired and the cost function may beapplied on one or more regions of the image. The outcome of the applyingof the cost function on the one or more regions may be evaluated to seewhich region should be selected—or whether a previous selection of theregion was appropriate.

The subset of pixels may be denoted [m_(i), n_(i)], where i=1 . . . k isthe index of a pixel in that subset. For example, I_(T)(x) may bedefined as a weighted sum of the intensities in the pixels subset[m_(i), n_(i)].

According to another example, I_(T)(x) is defined as the image contrastfor the pixel subset I=[m_(i), n_(i)], given by:

${I_{T}(x)} = \frac{{{Percentile}\left( {I,90} \right)} - {{Percentile}\left( {I,10} \right)}}{{{Percentile}\left( {I,90} \right)} + {{Percentile}\left( {I,10} \right)}}$

where Percentile(I,p) returns the gray level value corresponding to thep-th percentile of the image I. To clarify, the 90^(th) and 10^(th)percentiles provided are just an example, and other values of p can beused for other cases.

There may be provided any cost function. The cost function should beapplied on one or more properties of pixels of the subset of pixels—oron a larger group of pixels (for example—on pixels of the entire image).

The cost function may take into account a signal to noise ratio (SNR) ofpixels of the subset, or an intensity of pixels of the subset.

The cost function may take into account multiple properties of pixels ofthe subset of pixels.

FIG. 2C shows exemplary transmission curves obtained with system 200 for3 different tunable filter states: 790, 810, and 830 nm. These statescan cover a possible drift of ±20 nm in Osram's 810 nm LED SFH 4780S0.

FIG. 3 is a flow chart illustration of operations carried out accordingto some examples of the presently disclosed subject matter. Operationsdescribed with reference to FIG. 3 can be carried out for example by asystem designed according to the principles described above withreference to FIGS. 2A-2C.

The filter is set to an initial state in step 300 (e.g. by filter drivermodule 210). An image is obtained (e.g. by image acquisition module 212)in the initial state and in step 302 the cost function is calculated(e.g. by optimizer module 216) for example according to one of thefunctions described above. The filter is then set to a next state instep 304 (e.g. by filter driver module 210). An image is obtained (e.g.by image acquisition module 212) in the respective next state and, instep 306, the cost function is calculated (e.g. by optimizer module 216)for example as described above. Steps 304 and 306 are repeated N−1times, where N≥2 (e.g. with controller 208).

In step 308, based on the cost function values calculated for each ofthe different (N) states, a particular state out of the N states isselected.

For example, the selected state can be the one that maximizes the costfunction (e.g. by optimizer module 216), and in step 310 the filter istuned to the selected state (e.g. by filter driver module 210). As analternative, the cost function can be calculated each time (e.g. byoptimizer module 216) and compared to the previous result, and theresult with the higher cost function value is maintained with thecorresponding filter state and image, while the other result isdiscarded (e.g. by optimizer module 216). The end of this imagingsequence provides an optimal image and filter state.

Various calibrations of the system may be carried out one or more times,as required in the same way as described in the flow chart in FIG. 3.For manufacturing tolerances, a calibration procedure or process can bedone only once for each projector imaging system, and the optimal filterstate can be stored in the memory module.

As an example of a possible optimization, FIG. 4 depicts the costfunction I_(T) at several of the filter's tunable states. A fitpolynomial could then be applied to such point, and the optimal state atwhich I_(T) is at maximum, could be derived from it.

In summary, according to a method disclosed herein, a tunable filter'sspectral response (for example the spectral response of a MEMS tunablefilter) may be tuned to match the drift of the light source in aprojector imaging system, and specifically in an IR projector imagingsystem, and thus it enables us to achieve a FWHM closer to that of thelight source. Each time the system is used, one can compensate for thecurrent drift of the light source and find the filter state in which thefilter spectra overlaps that of the light source. A method disclosedherein allows one to considerably improve the performance of the imagingsystem by reducing light leakage, thus also improving the ER value ofthe system.

Applying a method disclosed herein to the example discussed in theBackground section (for Osram's 810 nm LED SFH 4780S0), the ER valuecould be considerable improved by at least 25%. In another example withstructured light systems which use a projected light pattern, reducingthe ambient light leakage increases the contrast of the projectedpattern and thus improves the system performance.

In some imaging system 200 may include a programmable computer device(e.g. controller 208), capable of being configured (e.g. programmed) toimplement a method of auto-homing a tunable filter in an imagining thatincludes a light projected as disclosed herein above including withreference to FIG. 3. Once a the image system (e.g. controller 208) isprogrammed to perform particular functions pursuant tocomputer-executable instructions from program software that implementsthe method disclosed herein, it in effect becomes a special purposecomputer particular to some embodiments of the method of the presentlydisclosed subject matter.

The methods and/or processes disclosed herein may be implemented as acomputer program product such as, for example, a computer programtangibly embodied in a data storage device, for example, in anon-transitory computer-readable or non-transitory machine-readablestorage device, for execution by or to control the operation of, a dataprocessing apparatus including, for example, one or more programmableprocessors and/or one or more computers. The term “non-transitory” isused to exclude transitory, propagating signals, but to otherwiseinclude any volatile or non-volatile computer memory technology suitableto the application including, for example, distribution media,intermediate storage media, execution memory of a computer, and anyother medium or device capable of storing for later reading by acomputer program implementing some embodiments of a method of thepresently disclosed subject matter.

There may be provided a system that may include an image sensor, atunable spectral filter, the tunable spectral filter may be positionedin a common optical path between an object and the image sensor—so thatthe camera images the object through the tunable spectral filter. Thesystem may also include a controller that is configured and operable toset the tunable spectral filter in a plurality of operation statescorrelated with a plurality of spectral bands.

The system may also include a non-tunable spectral filter that may beintegrated into the common optical path. The non-tunable spectral filtermay have one or more spectral transmission windows.

Different operation states of the tunable spectral filter may differfrom each other by the transmitted radiation bands such as but notlimited to red, clear/white, infra-red, near-infra-red or any othercolor.

The sensor may be monochromatic or non-monochromatic.

The tunable spectral filter may be a Fabry-Perot tunable spectralfilter, may be a MEMS based Fabry-Perot tunable spectral filter, and thelike.

The controller may change the operation states of the filter in apre-defined manner—for example according to a pre-defined exposurescheme.

The controller may process one or more image acquired by the camera anddetermine to change the operation states of the filter based on anoutcome of the processing of the image. The determining may beresponsive to history of previous changes of the operation states of thetunable spectral filter.

For example—if certain previous changes succeeded to improve theacquired image—then such certain previous change may be applied.

For example—if the change is required because of a lack ofdifferentiation between different objects—the changes that assisted in adifferentiation may be applied. The change may be selected based on thefeature (such as a color shared by the different objects) of thedifferent objects. If, for example, two objects share a same color (forexample red) then the tunable spectral filter may be set to reject thered color and allow another spectral band to be passed by the tunablespectral filter. If, for example, one of these objects may becharacterized by another color that is not included in anotherobject—then the tunable spectral filter may be set to pass that othercolor.

There may be provided an adaptive process for determining, based on oneor more attributes of images, the required new operation state of thetunable spectral filter.

FIG. 5 illustrates an example of general operation of the tunablefilter. A sequence of images (frames) 11, 12, 13, 14, 15, 16 areacquired at different points of time at one or more operation states ofthe tunable spectral filter. As can be appreciated, these images may beacquired at the same operation state, namely images that are acquired atdifferent time points by sensing the same wavelength band. However,images at different time points may be acquired at different operationsstates, namely images that are acquired by sensing different wavelengthbands. In the example provided in FIG. 5 first frame 11 was taken at afirst color (first setting of the tunable filter). Second frame 12 wastaken at a second color (second setting of the tunable filter). Thirdframe 13 was taken at a second color (second setting of the tunablefilter). Fourth frame 14 was taken at a second color (second setting ofthe tunable filter). Fifth frame 15 was taken at a third color (thirdsetting of the tunable filter). Sixth frame 16 was taken at a thirdcolor (third setting of the tunable filter). (@Some one requested toamend this—although this description and follows the figures of theprovisional patent)

FIG. 6 illustrates an example of a system that includes an image sensorsuch as camera 30, a tunable spectral filter 40, the tunable spectralfilter may be positioned in a common optical path between an object andthe image sensor—so that the camera (having a field of view FOV) imagesthe object through the tunable spectral filter. The system also includescontroller 50 that is configured and operable to set the tunablespectral filter in a plurality of operation states correlated with aplurality of spectral bands. Images acquired by the camera 30 areprocessed by processor 20.

Processor 20 may determine the next operation states of the tunablespectral filter based on a predefined scheme, based on outcome of aprocessing of an image, based on history, and the like.

Additionally or alternatively the processor may also determineparameters of the camera such as an exposure scheme.

FIG. 7 illustrates method 60 that includes multiple steps for processingan image and determining an exposure scheme and/or a new operation state(color set) of the tunable spectral filter.

Method 60 may include the following steps:

Step 61 of computing statistics (or any other score) related to theimage or to any part of the image. The statistics may refer to anyparameter for example intensity or contract) of pixels of the image (ora subset of the pixels of the image). Any statistical function ornon-statistical function may be applied.

Step 64 of computing new exposure scheme and/or new color set (fortuning the tunable filter).

Step 65 of capturing new frames.

Step 66 of setting the tunable filter to the desired exposure scheme.This may include continuing acquiring images.

The state that provided the best or better score of at least onecriterion of the image is set for continuing the imaging process.Alternatively, the state which provided a score above a predeterminedthreshold at least one image criterion is maintained for continuing theimaging process.

Step 61 is followed by step 64 that is followed by step 65 that isfollowed by step 66. Step 64 may be fed by step 62 of providing apredefined exposure scheme and/or color set in memory. Step 64 may befed by step 63 of receiving data from external sensors.

For example—

Let's take as a baseline example an automotive camera with red and clearexposures. Assume the baseline exposure scheme is clear, clear, clear,red [repeating].

Step 61—compute contrast per image using standard algorithms. Ifcontrast is below the threshold, switch to a different wavelength.

Another example—compute average intensity, if too low, set tunablespectral filter to pass Infrared (IR)—allow the camera to capture IRimages instead of/on top of clear/red exposures.

Step 64—in day time, plenty of light, can adjust to multiple repetitionsof two images of clear (set tunable spectral filter to pass all)followed by one image of red (set tunable spectral filter to pass red).

In night time, may need five clear images followed by red image(changing the operation state of the tunable spectral filteraccordingly)

Yet for another example—On a highway [high speed], no need in redexposures. In urban environment, where plenty of traffic lights, needmore [red, green, clear] to better distinguish the color.

In fog, capture a clear image followed by an IR image and then a redimage (and repeat).

Step 63 may provide inputs from sensors such as—speed meter, GPS,etc—the sensors are used to determine the scenario—urban/highwayenvironment and the like. The scenario will determine the sequences ofimages (operation modes of the tunable spectral filter).

Step 65—there may be a need to adjust vision algorithms per new colors[for example—processor may be required to identify traffic light modeusing red and green exposures].

Step 66—once processing is done, driving-related operation can be donesuch as break/accelerate and the like.

FIG. 8 illustrates method 80 that includes multiple steps for processingan image and determining a new operation state of the tunable spectralfilter.

Method 80 may include at least one of the following steps:

Step 82 of acquiring an image at a certain spatial filter state orreceive an image that was acquired at the certain spatial filter state.Step 82 is followed by step 84.

Step 84 of processing the image and determining a need to change thespatial filter state based on one or more image attribute (of the imagesacquired or received during step 82). The determining may be based onthe predetermined threshold. That could be any threshold that wasdefined and once the threshold is determined the process stops and thesystem is set with the state that reached the predetermined threshold.If the condition of the threshold is not met, the system continues tothe next state until reaches the threshold.

If there is no need to change the certain spatial filter state then step84 may be followed by step 82.

If there is a need to change the certain spatial filter state then step84 may be followed by step 88 of changing the spatial filter state. Step88 may be followed by step 82.

Step 84 may be responsive to history of previous filter state changes(step 86).

FIG. 9 illustrates method 90 that includes multiple steps for processingan image and determining a new operation state of the tunable spectralfilter.

Method 90 may include at least one of the following steps:

Step 92 of acquiring an image at a certain spatial filter state orreceive an image that was acquired at the certain spatial filter state.Step 92 is followed by step 94.

Step 94 of processing the image and determine a need to change thespatial filter state based on one or more image attribute (of the imagesacquired or received during step 92)—if there is a sufficientprobability that separate objects are indistinguishable from each other.

If there is no need to change the certain spatial filter state then step94 may be followed by step 92.

If there is a need to change the certain spatial filter state then step94 may be followed by step 98 of changing the spatial filter state. Step98 may be followed by step 92.

Step 94 may be responsive to history of previous filter state changes(step 96).

FIG. 10 illustrates method 70 that includes multiple steps forprocessing an image and determining a new mapping between physicalfilters and virtual filters based on one or more image attributes. Themapping map of the physical spectral response of the tunable spectralfilter to virtual filters. See, for example PCT patent applicationWO2017/017684.

Method 70 may include at least one of the following steps:

Step 72 of acquiring an image at a certain spatial filter state orreceive an image that was acquired at the certain spatial filter state.Step 72 is followed by step 74.

Step 74 of processing the image and determining (for example—based onthe predefined threshold) a need to change a mapping between physicalfilters and virtual filters based on one or more image attribute (of theimages acquired or received during step 72)—if there is a sufficientprobability that separate objects are indistinguishable from each other.

If there is no need to change the certain spatial filter state then step74 may be followed by step 72.

If there is a need to change the certain spatial filter state then step74 may be followed by step 78 of changing the spatial filter state. Step78 may be followed by step 72.

Step 74 may be responsive to history of previous filter state changes(step 76).

FIG. 11 illustrates a method 400 for setting a tunable filter.

Method 400 may start by step 410 of tuning, at different points in time,a tunable filter to different states for passing different frequencyranges.

The different states may include or can consist of a first state and asecond state.

The different states may include at least three states. Step 410 may beexecuted in parallel to step 420.

Step 420 may include acquiring, at each state (of the different steps ofstep 410) at least one image of an object, by an image sensor, toprovide different images of the object, wherein the acquiring occurswhile a radiation source illuminates the object with radiation.

The radiation source may exhibit a frequency drift.

The tunable filter may be a narrowband filter having a bandwidth thatequals a bandwidth of the radiation.

The tunable filter may be a narrowband filter having a bandwidth thatdiffers from a bandwidth of the radiation.

Step 420 may be followed by step 430.

Step 430 may include selecting a selected setting of the tunable filterbased on one or more properties of one or more images of the differentimages.

The one or more properties may be a signal to noise ratio.

The one or more properties may be a contrast.

Step 430 may include selecting the selected image to fulfill a desiredrelationship between (a) radiation from the object that originated fromthe radiation source and is sensed by the image sensor, and (b) ambientradiation sensed by the image sensor.

The desired relationship may be having a maximal amount of radiationfrom the object that originated from the radiation source and a minimalamount of the ambient radiation.

The desired relationship may be a maximal ratio between (a) an intensityof the radiation from the object that originated from the radiationsource and (b) an intensity of the ambient radiation.

Step 430 may include selecting a selected setting of the tunable filterthat “follows” the frequency drift of the radiation source.

The tunable filter may be a narrowband filter having a bandwidth thatmay be smaller than 300 nanometer.

Step 430 may include applying a cost function on the one or moreproperties of a subset of pixels of the one or more images.

The cost function may be an average intensity of pixels of the subset.

The method may include selecting pixels that belong to the subset.

Step 430 may be followed by step 440 of acquiring images while thetunable filter may be set to the selected setting.

FIG. 12 illustrates a method 500 for autonomously tuning a tunablefilter in an imaging system.

Method 500 may start by step 510.

Step 510 may include tuning the tunable filter through a plurality offilter states within at least one wavelength band.

The tunable filter may pass wavelengths of the visible and IR spectrum.

The tunable filter may be a narrowband filter having a bandwidth that issmaller than 300 nanometer.

The tunable filter may be a MEMS-based filter.

The tunable filter may be a MEMS-based filter that is an etalon.

Step 510 may include setting the tunable filter at a preliminary statewithin the certain wavelength band.

The certain wavelength band may range between 400-1000 nm. However,other wavelength bands may be provided—for example SWIR and LWIR ranges(up to 15 μm).

Step 510 may be preceded by receiving data indicative of environmentalconditions. Step 510 may include setting the tunable filter at apreliminary state based on the data indicative of environmentalconditions.

Step 510 may be executed in parallel to step 515.

Step 515 may include acquiring, by an image sensor, at each state of theplurality of states, at least one image of an object, to providedifferent images of the object. Step 515 may be executed in parallel tostep 510.

Step 515 may be executed while at least some of the field of view of thetunable filter is illuminated within a certain wavelength sub-band witha desired wavelength band. Step 510 may include determining theplurality of states within the certain wavelength band according to thedesired wavelength sub-band and an expected drift around the desiredwavelength sub-band.

Step 520 may include calculating for each state of the plurality ofstates a state related score, the state related score being indicativeof one or more properties of at least one subset of pixels of the atleast one image acquired at the state, to provide a plurality of staterelated scores. Step 520 may follow steps 510 and 515—but may startafter the acquisition of the first image.

The one or more properties of the imaging output may include at leastone out of intensity, contrast, SNR, sharpness, noise, color accuracy,dynamic range, distortion, uniformity, chromatic aberration, flare,color Moire, artifacts, compression, and color gamut.

The score may be calculated based on imaging data obtained from one ormore subsets of pixels of the image sensor.

The score may be calculated by applying a function maximum, the functionmay be constructed based on one or more of imaging output properties.

The score may be calculated by applying a predetermined threshold.

The score may be calculated by applying a cost function.

The score may be indicative of ambient light absorbed by the imagesensor relative to light in a desired wavelength band absorbed by theimage sensor.

Step 520 may be followed by step 530.

Step 530 may include selecting, based on at least one of the pluralityof state related scores, a particular filter state that satisfies adesired score criterion.

Step 530 may be followed by step 540.

Step 540 may include setting the tunable filter for capturing images inthe particular filter state.

Step 540 may be followed by acquiring one or more images.

Instead of assigning a state related score (per state) there may beassigned an image score per image (or per a subset of pixels of animage).

FIG. 13 illustrates method 600.

Method 600 may start by step 610 of tuning, by a controller of animaging system, the tunable filter to a first plurality of filterstates; wherein the spectral response of the tunable filter differs fromone filter state to another.

Step 615 may be executed in parallel to step 610 and may includeacquiring, by an image sensor of the imaging system, at each state ofthe plurality of states, at least one image of an object, to provide asecond plurality of images.

Steps 610 and 615 may be followed by step 620 of calculating, by thecontroller, an image score, for each image of the second plurality ofimages, that is indicative of one or more properties of the image, toprovide a second plurality of image scores. Step 620 may start after theacquisition of any image.

Step 620 may be followed by step 630 of determining, by the controller,based on the second plurality of images scores, a desired state of thetunable filter that satisfies a desired image score criterion.

Step 630 may be followed by step 640 of setting the tunable filter tothe desired state, for capturing images, by the image sensor while thetunable filter is set to the desired state.

FIG. 14 illustrates an imaging system 700 that includes imaging sensor710, controller 720 and tunable filter 730. FIG. 15 illustrates animaging system 702 that also includes radiation source 750 andenvironmental conditions sensor 740.

Each one of imaging systems 700 and 702 may be configured to execute atleast some of the methods illustrated above.

Imaging sensor 710 may send imaging output (such as images, pixels ofimages) to controller 720.

Controller 720 may send imaging sensor 710 imaging commands foracquiring image data and/or for setting various imaging and/or imageoutput parameters (for example which pixels should be acquired and/oroutputted.

Controller may send tuning commands to tunable filter 730. Tunablefilter 730 may send feedback to the controller—such as the tunablefilter state.

Referring to FIG. 15—the controller may also receive environmentalconditions data from environmental conditions sensors 740.

The controller may or may not control the radiation source 750. Thecontrolling may include setting any parameter of the radiation—timing,frequency, intensity, polarization, and the like.

The various features and steps discussed above, as well as other knownequivalents for each such feature or step, can be mixed and matched byone of ordinary skill in this art to perform methods in accordance withprinciples described herein. Although the disclosure has been providedin the context of some embodiments and examples, it will be understoodby those of ordinary skill in the art that the disclosure extends beyondthe specifically described embodiments to some other embodiments and/oruses and obvious modifications and equivalents thereof. Accordingly, thedisclosure is not intended to be limited by the specific disclosures ofsome embodiments of the presently disclosed subject matter.

For example, any digital computer system can be configured or otherwiseprogrammed to implement a method disclosed herein, and to the extentthat a particular digital computer system is configured to implementsuch a method, it is within the scope and spirit of the disclosure. Oncea digital computer system is programmed to perform particular functionspursuant to computer-executable instructions from program software thatimplements a method disclosed herein, it in effect becomes a specialpurpose computer particular to some embodiments of the method disclosedherein. The techniques that may be necessary to achieve this are wellknown to those of ordinary skill in the art and thus are not furtherdescribed herein. The methods and/or processes disclosed herein may beimplemented as a computer program product such as, for example, acomputer program tangibly embodied in an information carrier, forexample, in a non-transitory computer-readable or non-transitorymachine-readable storage device and/or in a propagated signal, forexecution by or to control the operation of, a data processing apparatusincluding, for example, one or more programmable processors and/or oneor more computers. The term “non-transitory” is used to excludetransitory, propagating signals, but to otherwise include any volatileor non-volatile computer memory technology suitable to the applicationincluding, for example, distribution media, intermediate storage media,execution memory of a computer, and any other medium or device capableof storing for later reading by a computer program implementing someembodiments of a method of the presently disclosed subject matter. Acomputer program product can be deployed to be executed on one computeror on multiple computers at one site or distributed across multiplesites and interconnected by a communication network.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create for implementingthe functions/acts specified in the flowchart and/or block diagram blockor blocks. These computer readable program instructions may also bestored in a computer readable storage medium that can direct a computer,a programmable data processing apparatus, and/or other devices tofunction in a particular manner, such that the computer readable storagemedium having instructions stored therein includes an article ofmanufacture including instructions which implement aspects of thefunction/act specified in the flowchart and/or block diagram block orblocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

Unless otherwise stated, the use of the expression “and/or” between thelast two members of a list of options for selection indicates that aselection of one or more of the listed options is appropriate and may bemade.

It should be understood that where the claims or specification refer to“a” or “an” element, such reference is not to be construed as therebeing only one of that element.

It is appreciated that certain features of the presently disclosedsubject matter, which are, for clarity, described in the context ofseparate embodiments or example, may also be provided in combination ina single embodiment. Conversely, various features of the presentlydisclosed subject matter, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable sub-combination or as suitable in any other describedembodiment of the presently disclosed subject matter. Certain featuresdescribed in the context of various embodiments are not to be consideredessential features of those embodiments, unless the embodiment isinoperative without those elements.

All patents and patent applications mentioned in this application arehereby incorporated by reference in their entirety for all purposes setforth herein. It is emphasized that citation or identification of anyreference in this application shall not be construed as an admissionthat such a reference is available or admitted as related art.

The terms “including”, “comprising”, “having”, “consisting” and“consisting essentially of” are used in an interchangeable manner. Forexample—any method may include at least the steps included in thefigures and/or in the specification, only the steps included in thefigures and/or the specification. The same applies to the spectralimager and the mobile computer.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

In the foregoing specification, the presently disclosed subject matterhas been described with reference to specific examples of someembodiments of the presently disclosed subject matter. It will, however,be evident that various modifications and changes may be made thereinwithout departing from the broader spirit and scope of the presentlydisclosed subject matter as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under”and the like in the description and in the claims, if any, are used fordescriptive purposes and not necessarily for describing permanentrelative positions. It is understood that the terms so used areinterchangeable under appropriate circumstances such that someembodiments of the presently disclosed subject matter described hereinare, for example, capable of operation in other orientations than thoseillustrated or otherwise described herein.

Those of ordinary skill in the art will recognize that the boundariesbetween logic blocks are merely illustrative and that alternativeembodiments may merge logic blocks or circuit elements or impose analternate decomposition of functionality upon various logic blocks orcircuit elements. Thus, it is to be understood that the architecturesdepicted herein are merely exemplary, and that in fact many otherarchitectures can be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.

Furthermore, those of ordinary skill in the art will recognize thatboundaries between the above described operations merely illustrative.The multiple operations may be combined into a single operation, asingle operation may be distributed in additional operations andoperations may be executed at least partially overlapping in time.Moreover, alternative embodiments may include multiple instances of aparticular operation, and the order of operations may be altered invarious other embodiments.

Also for example, in some embodiments, the illustrated examples may beimplemented as circuitry located on a single integrated circuit orwithin a same device. Alternatively, the examples may be implemented asany number of separate integrated circuits or separate devicesinterconnected with each other in a suitable manner.

Also, the examples, or portions thereof, may implemented as soft or coderepresentations of physical circuitry or of logical representationsconvertible into physical circuitry, such as in a hardware descriptionlanguage of any appropriate type.

Also, the presently disclosed subject matter is not limited to physicaldevices or units implemented in non-programmable hardware but can alsobe applied in programmable devices or units able to perform the desireddevice functions by operating in accordance with suitable program code,such as mainframes, minicomputers, servers, workstations, personalcomputers, notepads, personal digital assistants, electronic games,automotive and other embedded systems, cell phones and various otherwireless devices, commonly denoted in this application as ‘computersystems’.

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘including’ does notexclude the presence of other elements or steps then those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one as or more than one. Also, the use of introductory phrases suchas “at least one” and “one or more” in the claims should not beconstrued to imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to presently disclosed subject matterscontaining only one such element, even when the same claim includes theintroductory phrases “one or more” or “at least one” and indefinitearticles such as “a” or “an.” The same holds true for the use ofdefinite articles. Unless stated otherwise, terms such as “first” and“second” are used to arbitrarily distinguish between the elements suchterms describe. Thus, these terms are not necessarily intended toindicate temporal or other prioritization of such elements the mere factthat certain measures are recited in mutually different claims does notindicate that a combination of these measures cannot be used toadvantage.

Any system, apparatus or device referred to this patent applicationincludes at least one hardware component.

While certain features of the presently disclosed subject matter havebeen illustrated and described herein, many modifications,substitutions, changes, and equivalents will now occur to those ofordinary skill in the art. It is, therefore, to be understood that theappended claims are intended to cover all or most such modifications andchanges as fall within the true spirit of the presently disclosedsubject matter.

Any combination of any component and/or unit of spectral imager that isillustrated in any of the figures and/or specification and/or the claimsmay be provided.

Any combination of any spectral imager illustrated in any of the figuresand/or specification and/or the claims may be provided.

Any combination of any set of spectral imager s illustrated in any ofthe figures and/or specification and/or the claims may be provided.

Any combination of steps, operations and/or methods illustrated in anyof the figures and/or specification and/or the claims may be provided.

Any combination of operations illustrated in any of the figures and/orspecification and/or the claims may be provided.

Any combination of methods illustrated in any of the figures and/orspecification and/or the claims may be provided.

While this disclosure has been described in terms of certain embodimentsand generally associated methods, alterations and permutations of theembodiments and methods will be apparent to those of ordinary skill inthe art. The disclosure is to be understood as not limited by thespecific embodiments described herein, but only by the scope of theappended claims.

While this disclosure has been described in terms of certain embodimentsand generally associated methods, alterations and permutations of theembodiments and methods will be apparent to those of ordinary skill inthe art. The disclosure is to be understood as not limited by thespecific embodiments described herein. For example, while the projectorimaging system and its method of use are described in detail withreference to an IR light source and IR range tunable filter, such systemand method may be equally applicable in wavelength ranges other than IR,for example the visible range.

All references mentioned in this application are hereby incorporated byreference in their entirety for all purposes set forth herein. It isemphasized that citation or identification of any reference in thisapplication shall not be construed as an admission that such a referenceis available or admitted as related art.

1. An imaging system, the system comprising: an image sensor; a tunablefilter; and a controller operatively connected to the tunable filter andto the image sensor, wherein the imaging system is configured to: tunethe tunable filter to a plurality of filter states within at least onewavelength band; wherein the image sensor is configured to: acquire, ateach state of the plurality of states, at least one image of an object,to provide different images of the object; wherein the controller isconfigured to: calculate a state related score, for each state that isindicative of one or more properties of at least one subset of pixels ofthe at least one image acquired at the state, to provide a plurality ofstate related scores; and determine, based on at least one of theplurality of state related scores, a desired state of the tunable filterthat satisfies a desired state related score criterion; and set thetunable filter to the desired state.
 2. The imaging system of claim 1,wherein the one or more properties includes at least one out ofintensity, contrast, SNR, sharpness, noise, color accuracy, dynamicrange, distortion, uniformity, chromatic aberration, flare, color moire,artifacts, compression, and color gamut.
 3. (canceled)
 4. The imagingsystem of claim 1, wherein each state related score is calculated basedon data obtained from a subset of pixels of the at least one subset ofpixels.
 5. The imaging system of claim 1, wherein the controller isconfigured to calculate the state related score of each state byapplying a maximum function on the one or more properties of the atleast one subset of pixels of the at least one image acquired at thestate.
 6. The imaging system of claim 1, wherein the controller isconfigured to calculate the state related score of each state bycomparing the one or more properties of the at least one subset ofpixels of the at least one image acquired at the state to one or morepredetermined thresholds.
 7. (canceled)
 8. The imaging system of claim1, wherein the controller is configured to calculate the state relatedscore of each state to be indicative of ambient light absorbed by theimage sensor relative to light in a desired wavelength band absorbed bythe image sensor.
 9. The imaging system of claim 1, wherein thecontroller is configured to set the tunable filter to a preliminarystate within the at least one wavelength band.
 10. The imaging system ofclaim 1, wherein the imaging system is configured to receive dataindicative of environmental conditions and wherein the controller isconfigured to set the tunable filter at a preliminary state based on thereceived data indicative of the environmental conditions.
 11. (canceled)12. The imagining system of claim 1, the imaging system furthercomprising: a light source that is configured to illuminate the objectwith radiation in a desired wavelength band, wherein the plurality offilter states are determined according to the desired wavelength band ofthe light source and an expected drift around the desired wavelengthband.
 13. The imaging system of claim 1, wherein the tunable filter is anarrowband filter having a bandwidth that is smaller than 300 nanometer.14. The imaging system claim 1, wherein the at least one wavelength bandincludes the visible and/or IR wavelength band, and wherein the tunablefilter is an etalon MEMS-based filter.
 15. (canceled)
 16. A method ofautonomously tuning a tunable filter in an image system, the methodcomprising: using a controller for: tuning the tunable filter through aplurality of filter states within at least one wavelength band;acquiring, by an image sensor, at each state of the plurality of states,at least one image of an object, to provide different images of theobject; calculating for each state of the plurality of states a staterelated score, the state related score being indicative of one or moreproperties of calculate a state related score, for each state that isindicative of one or more properties of at least one subset of pixels ofthe at least one image acquired at the state, to provide a plurality ofstate related scores; and selecting based on at least one of theplurality of state related scores a particular filter state thatsatisfies a desired score criterion; and setting the tunable filter forcapturing images in the particular filter state.
 17. The method of claim16, wherein the one or more properties of the imaging output includesone or more image quality factors selected from a list consisting ofintensity, contrast, SNR, sharpness, noise, color accuracy, dynamicrange, distortion, uniformity, chromatic aberration, flare, color moire,artifacts, compression, Dmax and color gamut.
 18. (canceled)
 19. Themethod claim 16, wherein the calculating of a state related score, isbased on imaging data obtained from a subset of pixels of the at leastone subset of pixels.
 20. The method of claim 16, the method furthercomprising: calculating the state related score of each state byapplying a maximum or cost function on the one or more properties of theat least one subset of pixels of the at least one image acquired at thestate.
 21. The method of claim 16, the method further comprising:calculating the state related score of each state by comparing the oneor more properties of the at least one subset of pixels of the at leastone image acquired at the state to one or more predetermined thresholds.22. (canceled)
 23. The method of claim 16, wherein the state relatedscore of each state is indicative of ambient light absorbed by the imagesensor relative to light in a desired wavelength band absorbed by theimage sensor.
 24. (canceled)
 25. The method of claim 16, the methodfurther comprising: receiving data indicative of environmentalconditions; and setting the tunable filter at a preliminary state basedon the data indicative of environmental conditions.
 26. The method ofclaim 16, the method further comprising: illuminating within the certainwavelength band with a desired wavelength band; and determining theplurality of states within the certain wavelength band according to thedesired wavelength band and an expected drift around the desiredwavelength band.
 27. (canceled)
 28. The method of claim 16, wherein thetunable filter is a narrowband filter having a bandwidth that is smallerthan 300 nanometer; wherein the tunable filter is configured to transmitwavelengths of the visible and/or IR spectrum; and wherein the tunablefilter is an etalon MEMS-based filter.
 29. (canceled)
 30. (canceled) 31.(canceled)
 32. (canceled)